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Solving the Small Sample Size Problem of LDA
- In: Proceedings of the 16 th International Conference on Pattern Recognition (ICPR’02
, 2002
"... The small sample size problem is often encountered in pattern recognition. It results in the singularity of the within-class scatter matrix Sw in Linear Discriminant Analysis (LDA). Different methods have been proposed to solve this problem in face recognition literature. Some methods reduce the dim ..."
Abstract
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Cited by 43 (0 self)
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The small sample size problem is often encountered in pattern recognition. It results in the singularity of the within-class scatter matrix Sw in Linear Discriminant Analysis (LDA). Different methods have been proposed to solve this problem in face recognition literature. Some methods reduce
Small Sample Size Performance of the Energy Detector
"... Abstract—We examine the small sample size performance of the energy detector for spectrum sensing in AWGN. By making use of the cube-of-Gaussian approximation of chi-squared ran-dom variables, we derive a novel, simple, and accurate analytical expression for the minimum number of samples required to ..."
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Cited by 4 (0 self)
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Abstract—We examine the small sample size performance of the energy detector for spectrum sensing in AWGN. By making use of the cube-of-Gaussian approximation of chi-squared ran-dom variables, we derive a novel, simple, and accurate analytical expression for the minimum number of samples required
Fukunaga-Koontz Transform for Small Sample Size Problems
, 2005
"... Abstract — In this paper, we propose the Fukunaga-Koontz Transform (FKT) as applied to Small-Sample Size (SSS) problems and formulate a feature scatter matrix based equivalent of the FKT. We establish the classical Linear Discriminant Analysis (LDA) analogy of the FKT and apply it to a SSS situation ..."
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Abstract — In this paper, we propose the Fukunaga-Koontz Transform (FKT) as applied to Small-Sample Size (SSS) problems and formulate a feature scatter matrix based equivalent of the FKT. We establish the classical Linear Discriminant Analysis (LDA) analogy of the FKT and apply it to a SSS
Gesture Recognition Under Small Sample Size
, 2007
"... Abstract. This paper addresses gesture recognition under small sample size, where direct use of traditional classifiers is difficult due to high dimensionality of input space. We propose a pairwise feature extraction method of video volumes for classification. The method of Canonical Correlation Ana ..."
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Cited by 8 (2 self)
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Abstract. This paper addresses gesture recognition under small sample size, where direct use of traditional classifiers is difficult due to high dimensionality of input space. We propose a pairwise feature extraction method of video volumes for classification. The method of Canonical Correlation
Error Rate Estimation for Small Sample Sized Problems
"... Participating in computer research and development, especially in artificial intelligence and machine learning fields, and working in an environment that provides continuous motivation for problem solving and knowledge expansion. ..."
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Participating in computer research and development, especially in artificial intelligence and machine learning fields, and working in an environment that provides continuous motivation for problem solving and knowledge expansion.
A Gradient Linear Discriminant Analysis for Small Sample Sized Problem
, 2007
"... Abstract The purpose of conventional linear discriminant analysis (LDA) is to find an orientation which projects high dimensional feature vectors of different classes to a more manageable low dimensional space in the most discriminative way for classification. The LDA technique utilizes an eigenvalu ..."
Abstract
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Cited by 7 (3 self)
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an eigenvalue decomposition (EVD) method to find such an orientation. This computation is usually adversely affected by the small sample size problem. In this paper we have presented a new direct LDA method (called gradient LDA) for computing the orientation especially for small sample size problem
Approximate LDA Technique for Dimensionality Reduction in the Small Sample Size Case
, 2011
"... The regularized linear discriminant analysis (LDA) technique overcomes the small sample size (SSS) problem by adding a regularization parameter to the eigenvalues of within-class scatter matrix. However, it has some drawbacks. In this paper we address its drawbacks and propose an improvement. The pr ..."
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Cited by 2 (0 self)
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The regularized linear discriminant analysis (LDA) technique overcomes the small sample size (SSS) problem by adding a regularization parameter to the eigenvalues of within-class scatter matrix. However, it has some drawbacks. In this paper we address its drawbacks and propose an improvement
Testing Normality and Bandwith Estimation Using Kernel Method For Small Sample Size
"... This article aimed to study kernel method for testing normality and to determine the density function based on curve fitting technique (density plot) for small sample sizes. To obtain optimal bandwith we used Kullback-Leibler cross validation method. We compared the result using goodness of fit test ..."
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This article aimed to study kernel method for testing normality and to determine the density function based on curve fitting technique (density plot) for small sample sizes. To obtain optimal bandwith we used Kullback-Leibler cross validation method. We compared the result using goodness of fit
Results 1 - 10
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102,958
